Effects of climate change on the distribution of wild Akebia trifoliata

Abstract Understanding the impacts and constraints of climate change on the geographical distribution of wild Akebia trifoliata is crucial for its sustainable management and economic development as a medicinal material or fruit. In this study, according to the first‐hand information obtained from field investigation, the distribution and response to climate change of A. trifoliata were studied by the MaxEnt model and ArcGIS. The genetic diversity and population structure of 21 natural populations of A. trifoliata were studied by simple sequence repeat (SSR) markers. The results showed that the most important bioclimatic variable limiting the distribution of A. trifoliata was the Mean Temperature of Coldest Quarter (bio11). Under the scenarios SSP1‐2.6 and SSP2‐4.5, the suitable area of A. trifoliata in the world will remain stable, and the suitable area will increase significantly under the scenarios of SSP3‐7.0 and SSP5‐8.5. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 79.9–122.7°E and 21.5–37.5°N. Under the four emission scenarios in the future, the geometric center of the suitable distribution regions of Akebia trifoliata in China will move to the north. The clustering results of 21 populations of A. trifoliata analyzed by SSR markers showed that they had a trend of evolution from south to north.

A. trifoliata are imminent, which require us to make clear the geographical distribution of A. trifoliata. Previous studies on A. trifoliata mainly focused on pharmacological activities (Lu et al., 2019;Wang et al., 2020;Xue et al., 2008) and chemical components (Gao & Wang, 2006;Guan et al., 2008;Iwanaga et al., 2012;Wang et al., 2015), but there is a gap in understanding the natural distribution of A. trifoliata and its response to climate change.
Due to the progress of human society and global climate change, the ecological environment has been deeply affected. Climate, to a certain extent, determines the spatiotemporal distribution of species, and the change of species' geographic distribution also reflects the change of climate (Allen & Lendemer, 2016;Descombes et al., 2015). Climate change has a considerable impact on species' geographic distribution, phenology, and other ecological symptom and progresses, which will lead to the accelerated prosperity or disappearance of species (Acevedo et al., 2020;Lenoir et al., 2008).
Understanding how wild species respond to climate change is important for durative protection and supervision of wild species (Yi et al., 2016). Therefore, analyzing the response relationship between climatic variables and species (including Akebia trifoliata) is a key step to study the potential geographical distribution change of species, and a necessary way to study the impact of climate change on species survival.
Niche model can be used to predict the potential geographic distribution regions of species and habitat suitability evaluation, which can provide information for ecological research. It is an excellent choice to use the maximum entropy (MaxEnt) model to forecast the potential geographical distribution of species according to the existing species distribution information and various environmental data (Elith & Graham, 2009;Phillips, Anderson, et al., 2006;Phillips & Dudík, 2008. MaxEnt, based on the maximum entropy theory, has good accuracy even when the information on species distribution is insufficient (Saatchi et al., 2008;Yi et al., 2017). The model takes the climatic variables of the existing distribution points of species as the constraint conditions, supposing that the species will appear in all regions with suitable climatic conditions, but not in any regions not suitable for climatic conditions; the greater the entropy of species, the closer the probability distribution of species is to reality (Phillips, Anderson, et al., 2006). At present, MaxEnt has been used to forecast species distribution, and has been applied by many researchers to study the response relationship between species and climatic variables (Abdelaal et al., 2019;Li, Fan, et al., 2020;Yang et al., 2013;Zhang et al., 2019).
In this study, the MaxEnt model was used to predict the suitable distribution of A. trifoliata under different climate scenarios. The objectives of this study include: (1) to find and evaluate the key climatic variables affecting the distribution of A. trifoliata; (2) according to the current distribution data, to predict the suitable distribution regions for the growth of A. trifoliata; (3) to analyze the change trend of suitable distribution regions of A. trifoliata in different scenarios in the future (Focus on the distribution data concentration region, and the data records mainly occur in China.); (4) according to the prediction results and simple sequence repeat (SSR) markers (The genetic diversity and population structure of 21 Akebia trifoliata population were analyzed to determine its evolutionary relationship.), to analyze the migration trend of A. trifoliata in China.

| Species occurrence record
The occurrence record (Figure 1)

| Climatic variables
In this study, the climate scenarios used include current climate scenarios , past climate scenarios (the Last Interglacial, the  (Fick & Hijmans, 2017). According to CO 2 emissions, the four scenarios in the future can be classified as low emission scenario (SSP1-2.6), medium emission scenario (SSP2-4.5), medium-high emission scenario (SSP3-7.0), and high emission scenario (SSP5-8.5). The spatial resolution of all climate data is 2.5 minutes.
The time span of each climate scenario in the future is 80 years from 2021 to 2100, which is divided into four time scales: 2021-2040, 2041-2060, 2061-2080, and 2081-2100

| Prediction of Akebia trifoliata suitable distribution by MaxEnt
In this study, MaxEnt (MaxEnt 3.4.1) was used to predict the potential suitable distribution regions of A. trifoliata (Phillips, DudíK, et al., 2006

TA B L E 1 List of climate variables
ROC curve analysis is used to verify the accuracy of the MaxEnt model . In this method, the area under curve, namely AUC value (the value range is 0-1), is used to judge the prediction accuracy of the model. When the AUC value is >0.9, the accuracy of the prediction results is high (Phillips, Anderson, et al., 2006).
In order to avoid the overfitting of the model caused by the collinearity among variables and improve the operation efficiency, the climatic variables were screened after running the full variable prediction (Li, Fan, et al., 2020;Sillero & Barbosa, 2020). Using the remaining climatic variables after screening, recompile the MaxEnt model, and the calculation efficiency and prediction accuracy of the model will be improved. The screening process was as follows: 1. Calculate the correlation between climatic variables with the

Pearson correlation coefficient in SPSS (Statistical Product and
Service Solutions, version 26.0, Armonk, NY, USA) software (Table S1).
2. Delete variables (percent contribution is <1%) whose contribution percentage is less than the contribution threshold setting at the first full variable prediction (Table S2). Among the remaining variables with high correlation (the correlation coefficient is >0.8 or <−0.8), the variable with the highest contribution rate is retained as the variable used to recompile the model.

| Suitability division of distribution regions of Akebia trifoliata
The ASCII file exported by MaxEnt was converted into raster layer by using To Raster tool in ArcGIS, and the raster layer that contains the suitable distribution region of A. trifoliata was obtained. The fitness value (species existence probability) predicted by the model is continuous raster data, and the range of value is 0-1. The map layer that contains the suitable distribution region was divided into four grades by using the tool of Reclassify of ArcGIS and artificial grading method: no suitability (0 ≤ probability of existence < .15), low suitability (.15 ≤ probability of existence <.33), medium suitability (.33 ≤ probability of existence <.66), and high suitability (probability of existence >.66).

| Obtaining the geometric centers of distribution regions
Because the boundary of the suitable distribution regions was irregular, it is difficult to describe the migration of the suitable distribution regions through the change of the boundary. The method of this study was to use the change of geometric centers of suitable distribution regions to describe migration.

Analysis of the geometric centers of the distribution regions of
A. trifoliata involved three tools in ArcGIS: Raster Calculator, Raster Domain, and Mean Center. First, Raster Calculator was used to cut the raster layer that contains the suitable distribution regions of A. trifoliata, and the raster layer of suitable distribution regions were left. Then, Raster Domain was used to convert the raster layer of suitable distribution regions to plane geometry graphics. Finally, Mean Center was used to find the geometric centers of the plane geometry graphics.

| Genetic diversity and population structure of Akebia trifoliata
To further reveal the migration status of A. trifoliata community in China, the genetic diversity and population structure of 21 A. trifoliata natural populations were studied by using SSR markers (detailed information about SSR primers used in this experiment was shown in Table S3). We used 8 pairs of primers to detect 194 alleles in 578 individuals of 21 natural populations. The distance between individuals should be at least 50 meters to avoid collecting asexually reproduced individuals.

| Performance analysis of the model
The average AUC of the prediction results of this model was 0.935 ( Figure 2a), which indicates that accuracy of prediction results was high.

| Climatic variables analysis
According to the screening mechanism of the method part in this paper, combined with the contribution rate of 19 climatic variables (bio1-19) in MaxEnt model and the correlation test of them, the remaining 7 climatic variables (bio2, bio3, bio4, bio8, bio10, bio11 and bio12) were used to run MaxEnt model again.
Jackknife test (using AUC on test data) was used to analyze the impact of climatic variables on the prediction results to determine the importance of each climatic factor (Figure 2b). When a climatic variable was used alone, the four variables with greater gain were bio2, bio4, bio11, and bio12. Bio11 has the highest gain value. This shows that the above four climatic variables were the main influencing factors for predicting the suitable distribution regions of A. trifoliata. The response curves of four important climatic variables and the existence probability of A. trifoliata were shown in Figure 3.
When bio 2 is less than 14℃, the existence probability of A. trifoliata is >0, and with the continuous decrease of bio 2, the existence probability shows an overall upward trend, which indicates that stable temperature conditions are more conducive to the survival of A. trifoliata. When bio4 is equal to 655℃, the existence probability of A. trifoliata can reach a peak. If bio4 continues to decrease, the existence probability decreases rapidly and then increases slowly. When bio4 is less than 286℃, the existence probability will remain above .5. However, when bio4 increases on the basis of 655℃, the probability of existence will eventually become 0.
When bio11 is <−10℃, the existence probability of A. trifoliata is close to 0. When bio11 is >−0℃, the existence probability begins to increase and reaches the peak at 10℃. From this point, the existence probability decreases rapidly with the continuous increase of bio11. Bio12 is a precipitation related variable. When bio12 is <400 mm, the existence probability of A. trifoliata is close to 0. Then, with the continuous increase of bio12, the existence probability also increases. When bio12 reaches 4500 mm, the existence probability can reach more than 0.9.  Figure S1 shows the suitable distribution regions of A. trifoliata in the past. In the Last Interglacial scenario ( Figure S1a), the total suitable area of A. trifoliata was 661.15 × 10 4 km 2 . In the Last Glacial Maximum scenario (Figure S1b), the total suitable area was 910.81

| Current suitable distribution regions
× 10 4 km 2 . The total suitable area in these two scenarios was much smaller than that in the current scenario. In the Mid-Holocene scenario ( Figure S1c), the total suitable area was 1596.03 × 10 4 km 2 , slightly different from the total suitable area under the current scenario. However, in the Mid-Holocene scenario, the suitable distribution regions were mainly concentrated in the tropics. The detailed values of suitable area in the past were shown in Table S6.

| Main climatic variables affecting the distribution of Akebia trifoliata
Temperature and precipitation are two key factors limiting plant growth. When the climate factors related to temperature and precipitation change too much and exceed the current threshold of plant growth, it will lead to the migration of its population (Camille & Gary, 2003). Water is particularly important for plants. It not only has a significant impact on the photosynthesis of plants, but F I G U R E 6 The broken line diagram of the suitable area of Akebia trifoliata. (a) The total suitable area of Akebia trifoliata in the world. (b) The total suitable area of Akebia trifoliata in China also determines the growth status of plants. Therefore, precipitation is an important factor limiting the distribution of plants (Zhang et al., 2018). The longer growing season is helpful for the plant population to migrate to more suitable regions, and the increase of precipitation in the driest month can prolong the growth season (Vaganov et al., 1999). Extreme low and high temperatures also limit the growth of plants. Plants will suffer from freezing damage if they are exposed to low temperature for a long time, and the decrease of the lowest temperature in the coldest month will undoubtedly aggravate the damage (Harsch & HilleRisLambers, 2016). However, high temperature will destroy the water balance in plants, leading to protein condensation and accumulation of harmful metabolites (Lemmens et al., 2006). If the highest temperature in the warmest month increases, the growth of plants will be hindered to a certain extent.
In this study, the 19 variables used to predict the distribution of species belong to the category of temperature and precipitation factors. The results of Jackknife test showed that bio2, bio4, bio11, and bio12 were the four key variables limiting the distribution of A. trifoliata. Among them, the most critical variable is bio11, which belongs to the variable related to temperature factor.
The research on A. quinata shows that the key variable limiting its distribution is bio12 , which is related to precipitation factors. This indicates that the priority of temperature

| Change trend of suitable distribution regions of Akebia trifoliata
Species respond differently to climate change. In the future, the suitable area of A. trifoliata in the world will tend to be stable under low emission scenarios (SSP1-2.6) and medium emission scenarios (SSP2-4.5). In the medium-high emission scenarios (SSP3-7.0) and high emission scenarios (SSP5-8.5), the suitable area has increased significantly. In the study of two species of peony (Zhang et al., 2018), the suitable area of Paeonia rockii was increased in both of emission scenario (RCP2.6) and emission scenario (RCP8.5) (for a detailed description of the climate scenario, see www.carbo nbrief. org/cmip6 -the-next-gener ation -of-clima te-model s-expla ined).
The research on Tricholoma matsutake shows that in the scenario RCP8.5, its suitable area will be greatly reduced, and the distribution regions will even be fragmented (Guo et al., 2017). Under the four different emission scenarios in the future, the geometric center of the suitable distribution regions of A. quinata in East Asia continues to move to the northeast with the upgrading of the emission scenario, which is caused by the continuous reduction of the suitable area in China . Due to climate change, the suitable distribution area of species will also change (Hampe & Petit, 2005;Ramírez-Preciado et al., 2019;Thuiller et al., 2008).
The expansion or contraction of species distribution region, species migration, and even the fragmentation of distribution region is the reason for the change of geometric center of species distribution area Zhang et al., 2021). In the future scenario, the geometric centers of the suitable distribution regions of A. trifoliata move north, which may be because it has the trend of northward expansion.
The process of species migration and adaptation to new habitats must be accompanied by changes in climate factors, which will promote the genetic variation of species. The evolutionary relationship of 21 A. trifoliata populations in China may be related to the change of habitat during the migration of this species.

| CON CLUS IONS
Predicting how climate change will affect the distribution of A. trifoliata is of great significance for its conservation. The Mean Diurnal Range (bio2), the Temperature Seasonality (bio4), the Mean Temperature of Coldest Quarter (bio11), and the Annual Precipitation (bio12) are the main climatic variables that affect the distribution of A. trifoliata. In the future, the suitable area of A. trifoliata in the world will remain stable in low emission scenario (SSP1-2.6) and medium emission scenario (SSP2-4.5) and increase significantly in medium-high and high emission scenarios (SSP3-7.0 and SSP5-8.5). The geometric center of the distribution area of A. trifoliata in China will move to the north. There was a migration trend of A. trifoliata in China from south to north.

ACK N OWLED G M ENT
The authors gratefully acknowledge financial support from the Central

CO N FLI C T O F I NTE R E S T
The authors report no conflict of interest.